The Best AI (Artificial Intelligence) and SI (Super Intelligence) Q&A

The top three AI models and assistants in the world, ranked by general intelligence, benchmarks and industry usage, are from Google, Anthropic and OpenAI, led by their flagship models Gemini, Claude and GPT.

• Google (Gemini / Carbon). Google's top models, such as Gemini and its recent high-intelligence releases like Gemini Carbon, consistently trade or hold the #1 spot on global intelligence and benchmark leaderboards, while deeply integrating real-time data, web search and Google Workspace.
• Anthropic (Claude). The Claude family, including Claude Opus and Claude Fable, dominates specialized rankings for complex reasoning, long-context analysis (up to 1-million-token windows), writing, and software engineering and agent workflows.
• OpenAI (ChatGPT / GPT). OpenAI remains a global pioneer and user-base leader with its GPT series, such as GPT-6 Astra, recognized for broad ecosystem integrations, deep research capabilities and strong chain-of-thought processing.

Depending on whether you measure raw intelligence benchmarks, coding agents or active global consumer usage, competitors like DeepSeek or Perplexity also rank near the top for specific use cases.

Read moreIndependent AI leaderboardsAI models: prices and context windowsClaude Opus 5.5GPT-6 Astra

Link to this answer · Updated 2026-11-02

The phrase "Big 4 of AI" usually means one of two things, depending on whether you are talking about technology and model developers or about accounting and consulting firms.

1. The Big 4 tech and model companies. In model development, the "Big 4" are the giants building foundational models and scaling the AI ecosystem:

• OpenAI: creator of GPT-4 and ChatGPT, known for leading consumer and enterprise generative AI.
• Google (Alphabet): developer of the Gemini model family and DeepMind research.
• Anthropic: creator of the Claude model family, focused heavily on AI safety and enterprise alignment.
• Meta: leader in open-source AI through its LLaMA model series.

Major cloud providers also serve as the infrastructure platforms powering these models: Microsoft (a primary OpenAI partner) and Amazon (through AWS).

2. The Big 4 accounting and consulting firms. In professional services, the "Big 4" are the global accounting networks that have integrated AI into audit, tax and advisory work:

• Deloitte: uses tools like Zora AI and Omnia for automated financial trend analysis and auditing.
• PwC: operates platforms like GL.ai and partners deeply with OpenAI.
• EY (Ernst & Young): built the EY.ai platform and deployed large suites of tax and audit AI agents.
• KPMG: invests heavily in enterprise AI and platforms like Clara through strategic technology partnerships.

Read moreAI models by providerEnterprise and government AI

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An AI agent is a software system that uses an AI model to pursue a goal over several steps, choosing actions and calling tools such as search, code execution or other applications, instead of answering a single prompt. Vendors use the word loosely, so ask which decisions the model makes and which are fixed in code.

Because an agent acts on your behalf, the controls around it matter: narrow permissions, short-lived access, and an approval gate where a person must approve consequential steps such as deleting data, sending messages, spending money or deploying code.

Read moreWhat is an agentic workflow?What is an AI coding agent?How to evaluate AI agents

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The context window is the amount of text, code or other input an AI model can consider at one time when producing a response. Content outside the window is not seen by the model, which is why long tasks may need summarizing or retrieval. Several current models list windows of about one million tokens.

Read moreContext windows of 15 modelsGlossary

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The Model Context Protocol is an open-source standard for connecting AI applications to external systems such as data sources and tools. Anthropic announced it in November 2024. A shared connector standard makes integration easier, but it does not make any connected tool safe, so give an agent only the access its task needs.

Read moreHow agents connect to toolsGlossary

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Current AI systems are classified as "narrow AI" (designed for specific tasks like chat, translation or image recognition) or as emerging frontier models that some researchers argue approach artificial general intelligence (AGI): matching human-level capabilities across a broad range of tasks. True superintelligence, an entity that vastly outperforms the smartest human minds across every cognitive domain, remains a theoretical concept rather than a present reality.

Key perspectives on its status:

• Current capabilities. Existing AI models can process massive amounts of data and solve complex problems quickly, but they still show "jagged" reliability, lack true general reasoning and require human oversight.
• Timeline debate. Some researchers and commentators estimate that superintelligence could arrive within the coming years through recursive self-improvement, while many computer scientists emphasize that major technological and safety breakthroughs are still missing.
• Policy changes. The U.S. federal government has shifted official executive branch terminology to emphasize "Super Intelligence" to describe rapid advancements in frontier systems. This rebranding applies to current advanced AI, not to a newly born superintelligence.

Organizations like OpenAI continue to research the safety and alignment challenges of future systems, and researchers examine the implications of these growing capabilities.

Read moreSuperintelligence: the full encyclopedia articleSuperintelligence and today's toolsExecutive Order 14434, with the primary source

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AI is the broad field, and in practice means today's systems built for particular kinds of work, such as chat assistants, coding tools and image generators. AGI (artificial general intelligence) is a term for AI with human-level breadth across many kinds of task. There is no single agreed definition or test, so a claim that a system is or is not AGI should say which definition it means.

Superintelligence is a hypothetical level of machine intelligence that greatly exceeds human performance across most areas, and ASI (artificial superintelligence) is the same idea under another name. Definitions differ among researchers and institutions, and the term does not describe any product sold today.

Read moreAI vs AGI vs SI vs ASI: the differencesSuperintelligence: the full encyclopedia articleGlossary

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Executive Order 14434, "Inaugurating the Era of Super Intelligence", was signed on 29 September 2026 and published in the Federal Register (91 FR 63129) on 2 October 2026. It directs executive departments and agencies to use the terms "Super Intelligence" and "SI" in place of "Artificial Intelligence" and "AI" in official correspondence, public communications, websites, reports and other non-statutory documents, to the maximum extent permitted by law.

For the purposes of the order, the new terms mean the technologies and systems covered by the existing statutory definition of artificial intelligence. The order renames a term for federal use. By itself it does not set a new technical standard or change what any product can do, and it does not claim that a superintelligence now exists.

Read moreExecutive Order 14434: summary and primary sourcesThe AI-to-Super-Intelligence rebrand, explainedPolicy tracker

Link to this answer · Updated 2026-11-02

Through an API, models are priced per million tokens, with output tokens costing more than input tokens. On the list prices this site recorded on 24 October 2026, Claude Opus 5.5 is $4 for input and $20 for output per million tokens, GPT-6 Astra is $10 and $50, and Gemini 3.8 Flash is $0.75 and $3.75.

List prices change, and your real bill depends on how many tokens you send and receive, so check the vendor's pricing page before you budget. The cost calculator turns your own request volume into a monthly estimate for 15 models.

Read moreAI API cost calculatorHow LLM API pricing worksAI models: list prices and context windows

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This site has not run its own tests yet, so it does not name a winner. What it publishes instead is the evidence available: a ranked list of the most used AI coding tools of 2026 with the basis stated, the editor's ratings with a reason for each, and reviews of the main tools, each labelled by how it is sourced.

The right choice depends on your editor, your language, your budget and how much autonomy you want to give the tool, so shortlist two or three and try them on your own code.

Read moreTop AI coding tools by developer usageEditor's ratingsModel finder: five questions, up to three models to test

Link to this answer · Updated 2026-11-02

Top-tier AI models from OpenAI, Anthropic and Google lead current intelligence and IQ-style evaluations, with GPT-6.1 Sol and Opus 5.5 scoring around 141 on the AI IQ rankings at aiiq.org. According to that tracking data, the highest-scoring models are:

• GPT-6.1 Sol (OpenAI): about 141
• Opus 5.5 (Anthropic): about 141
• Gemini 4 Argon (Google): about 140
• GPT-6 Astra (OpenAI): about 140
• Gemini 4 Carbon (Google): about 139

Important context on AI "IQ".

• Not a human IQ. Standard human IQ tests (like Mensa) are designed for biological humans and rely on human cognitive architecture. When AI models "take" these tests, they are often evaluated on pattern recognition, logic and abstract reasoning benchmarks that mimic test questions.
• Contaminated training data. Many critics note that large language models may have met the test questions or similar logic puzzles in their web-scale training data, which makes direct comparisons to human scores controversial or misleading.
• Alternative benchmarks. Rather than traditional IQ, researchers evaluate AI with functional leaderboards such as FrontierMath, GPQA (graduate-level physics, chemistry and biology) and AIME (mathematical olympiad) to measure real problem-solving capability.

Read moreBenchmark contamination explainedReading benchmark claims criticallyIndependent AI leaderboardsGPT-6.1 Sol

Link to this answer · Updated 2026-11-02

Treat a benchmark score as a lead to check, not a verdict. A benchmark is a fixed set of tasks with a scoring rule, so a score describes performance on those tasks under those conditions, not on your work. Scores a vendor publishes about its own model are vendor-reported claims, and different vendors use different harnesses, effort settings and tools, which makes numbers from different pages hard to compare.

Public question sets can also leak into training data, a problem called benchmark contamination, so a high score may reflect memorization. Look for who ran the test, the conditions, the product version and the date.

Read moreReading benchmark claims criticallyBenchmark contamination explainedVendor-reported benchmark records

Link to this answer · Updated 2026-11-02